Chasing Accreted Structures within Gaia DR2 Using Deep Learning

Lina Necib, Bryan Ostdiek, Mariangela Lisanti, Timothy Cohen, Marat Freytsis, Shea Garrison-Kimmel

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release of Gaia (DR2). In this paper, we apply two clustering algorithms to identify velocity substructure within this catalog. We focus on the subset of stars with line-of-sight velocity measurements that fall in the range of Galactocentric radii r Î [6.5, 9.5] kpc and vertical distances z∣ < 3 kpc. Known structures such as Gaia Enceladus and the Helmi stream are identified. The largest previously unknown structure, Nyx, is a vast stream consisting of at least 200 stars in the region of interest. This study displays the power of the machine-learning approach by not only successfully identifying known features but also discovering new kinematic structures that may shed light on the merger history of the Milky Way.

Original languageEnglish (US)
Article number25
JournalAstrophysical Journal
Issue number1
StatePublished - Nov 1 2020

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


Dive into the research topics of 'Chasing Accreted Structures within Gaia DR2 Using Deep Learning'. Together they form a unique fingerprint.

Cite this